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IvanKobzarevIvanKobzarev
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[android][native_app] App example of linking to gradle deps native libs and torchscript CustomOp
1 parent 1e36f9e commit 0570874

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lines changed

‎NativeApp/app/CMakeLists.txt

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cmake_minimum_required(VERSION 3.4.1)
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set(TARGET pytorch_nativeapp)
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project(${TARGET} CXX)
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set(CMAKE_CXX_STANDARD 14)
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set(build_DIR ${CMAKE_SOURCE_DIR}/build)
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set(pytorch_testapp_cpp_DIR ${CMAKE_CURRENT_LIST_DIR}/src/main/cpp)
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file(GLOB pytorch_testapp_SOURCES
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${pytorch_testapp_cpp_DIR}/pytorch_nativeapp.cpp
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)
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add_library(${TARGET} SHARED
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${pytorch_testapp_SOURCES}
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)
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file(GLOB PYTORCH_INCLUDE_DIRS "${build_DIR}/pytorch_android*.aar/headers")
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file(GLOB PYTORCH_LINK_DIRS "${build_DIR}/pytorch_android*.aar/jni/${ANDROID_ABI}")
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target_compile_options(${TARGET} PRIVATE
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-fexceptions
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)
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set(BUILD_SUBDIR ${ANDROID_ABI})
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find_library(PYTORCH_LIBRARY pytorch_jni
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PATHS ${PYTORCH_LINK_DIRS}
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NO_CMAKE_FIND_ROOT_PATH)
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find_library(FBJNI_LIBRARY fbjni
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PATHS ${PYTORCH_LINK_DIRS}
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NO_CMAKE_FIND_ROOT_PATH)
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# OpenCV
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if(NOT DEFINED ENV{OPENCV_ANDROID_SDK})
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message(FATAL_ERROR "Environment var OPENCV_ANDROID_SDK set")
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endif()
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set(OPENCV_INCLUDE_DIR "$ENV{OPENCV_ANDROID_SDK}/sdk/native/jni/include")
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target_include_directories(${TARGET} PRIVATE
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"${OPENCV_INCLUDE_DIR}"
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${PYTORCH_INCLUDE_DIRS})
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set(OPENCV_LIB_DIR "$ENV{OPENCV_ANDROID_SDK}/sdk/native/libs/${ANDROID_ABI}")
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find_library(OPENCV_LIBRARY opencv_java4
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PATHS ${OPENCV_LIB_DIR}
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NO_CMAKE_FIND_ROOT_PATH)
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target_link_libraries(${TARGET}
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${PYTORCH_LIBRARY}
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${FBJNI_LIBRARY}
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${OPENCV_LIBRARY}
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log)

‎NativeApp/app/build.gradle

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apply plugin: 'com.android.application'
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repositories {
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jcenter()
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maven {
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url "https://oss.sonatype.org/content/repositories/snapshots"
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}
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}
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android {
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configurations {
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extractForNativeBuild
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}
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compileSdkVersion 28
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buildToolsVersion "29.0.2"
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defaultConfig {
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applicationId "org.pytorch.nativeapp"
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minSdkVersion 21
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targetSdkVersion 28
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versionCode 1
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versionName "1.0"
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externalNativeBuild {
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cmake {
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arguments "-DANDROID_STL=c++_shared"
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}
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}
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}
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buildTypes {
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release {
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minifyEnabled false
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}
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}
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externalNativeBuild {
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cmake {
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path "CMakeLists.txt"
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}
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}
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sourceSets {
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main {
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jniLibs.srcDirs = ['src/main/jniLibs']
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}
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}
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}
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dependencies {
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implementation 'com.android.support:appcompat-v7:28.0.0'
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implementation 'org.pytorch:pytorch_android:1.6.0-SNAPSHOT'
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extractForNativeBuild 'org.pytorch:pytorch_android:1.6.0-SNAPSHOT'
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}
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task extractAARForNativeBuild {
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doLast {
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configurations.extractForNativeBuild.files.each {
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def file = it.absoluteFile
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copy {
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from zipTree(file)
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into "$buildDir/$file.name"
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include "headers/**"
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include "jni/**"
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}
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}
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}
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}
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tasks.whenTaskAdded { task ->
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if (task.name.contains('externalNativeBuild')) {
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task.dependsOn(extractAARForNativeBuild)
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}
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}

‎NativeApp/app/src/main/AndroidManifest.xml

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<?xml version="1.0" encoding="utf-8"?>
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<manifest xmlns:android="http://schemas.android.com/apk/res/android"
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package="org.pytorch.nativeapp">
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<application
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android:allowBackup="true"
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android:label="PyTorchNativeApp"
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android:supportsRtl="true"
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android:theme="@style/Theme.AppCompat.Light.DarkActionBar">
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<activity android:name=".MainActivity">
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<intent-filter>
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<action android:name="android.intent.action.MAIN" />
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<category android:name="android.intent.category.LAUNCHER" />
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</intent-filter>
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</activity>
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</application>
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</manifest>

‎NativeApp/app/src/main/assets/.gitignore

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*
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*/
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!.gitignore

‎NativeApp/app/src/main/cpp/pytorch_nativeapp.cpp

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#include <android/log.h>
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#include <cassert>
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#include <cmath>
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#include <pthread.h>
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#include <unistd.h>
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#include <vector>
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#define ALOGI(...) \
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__android_log_print(ANDROID_LOG_INFO, "PyTorchNativeApp", __VA_ARGS__)
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#define ALOGE(...) \
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__android_log_print(ANDROID_LOG_ERROR, "PyTorchNativeApp", __VA_ARGS__)
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#include "jni.h"
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#include <opencv2/opencv.hpp>
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#include <torch/script.h>
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namespace pytorch_nativeapp {
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namespace {
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torch::Tensor warp_perspective(torch::Tensor image, torch::Tensor warp) {
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cv::Mat image_mat(/*rows=*/image.size(0),
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/*cols=*/image.size(1),
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/*type=*/CV_32FC1,
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/*data=*/image.data_ptr<float>());
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cv::Mat warp_mat(/*rows=*/warp.size(0),
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/*cols=*/warp.size(1),
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/*type=*/CV_32FC1,
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/*data=*/warp.data_ptr<float>());
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cv::Mat output_mat;
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cv::warpPerspective(image_mat, output_mat, warp_mat, /*dsize=*/{8, 8});
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torch::Tensor output =
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torch::from_blob(output_mat.ptr<float>(), /*sizes=*/{8, 8});
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return output.clone();
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}
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static auto registry =
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torch::RegisterOperators("my_ops::warp_perspective", &warp_perspective);
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template <typename T> void log(const char *m, T t) {
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std::ostringstream os;
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os << t << std::endl;
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ALOGI("%s %s", m, os.str().c_str());
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}
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struct JITCallGuard {
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torch::autograd::AutoGradMode no_autograd_guard{false};
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torch::AutoNonVariableTypeMode non_var_guard{true};
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torch::jit::GraphOptimizerEnabledGuard no_optimizer_guard{false};
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};
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} // namespace
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static void loadAndForwardModel(JNIEnv *env, jclass, jstring jModelPath) {
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const char *modelPath = env->GetStringUTFChars(jModelPath, 0);
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assert(modelPath);
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// To load torchscript model for mobile we need set these guards,
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// because mobile build doesn't support features like autograd for smaller
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// build size which is placed in `struct JITCallGuard` in this example. It may
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// change in future, you can track the latest changes keeping an eye in
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// android/pytorch_android/src/main/cpp/pytorch_jni_jit.cpp
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JITCallGuard guard;
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torch::jit::Module module = torch::jit::load(modelPath);
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module.eval();
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torch::Tensor x = torch::randn({4, 8});
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torch::Tensor y = torch::randn({8, 5});
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log("x:", x);
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log("y:", y);
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c10::IValue t_out = module.forward({x, y});
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log("result:", t_out);
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env->ReleaseStringUTFChars(jModelPath, modelPath);
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}
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} // namespace pytorch_nativeapp
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JNIEXPORT jint JNI_OnLoad(JavaVM *vm, void *) {
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JNIEnv *env;
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if (vm->GetEnv(reinterpret_cast<void **>(&env), JNI_VERSION_1_6) != JNI_OK) {
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return JNI_ERR;
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}
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jclass c = env->FindClass("org/pytorch/nativeapp/NativeClient$NativePeer");
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if (c == nullptr) {
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return JNI_ERR;
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}
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static const JNINativeMethod methods[] = {
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{"loadAndForwardModel", "(Ljava/lang/String;)V",
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(void *)pytorch_nativeapp::loadAndForwardModel},
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};
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int rc = env->RegisterNatives(c, methods,
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sizeof(methods) / sizeof(JNINativeMethod));
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if (rc != JNI_OK) {
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return rc;
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}
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return JNI_VERSION_1_6;
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}

‎NativeApp/app/src/main/java/org/pytorch/nativeapp/MainActivity.java

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package org.pytorch.nativeapp;
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import android.content.Context;
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import android.os.Bundle;
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import android.util.Log;
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import androidx.appcompat.app.AppCompatActivity;
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import java.io.File;
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import java.io.FileOutputStream;
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import java.io.IOException;
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import java.io.InputStream;
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import java.io.OutputStream;
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public class MainActivity extends AppCompatActivity {
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private static final String TAG = "PyTorchNativeApp";
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public static String assetFilePath(Context context, String assetName) {
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File file = new File(context.getFilesDir(), assetName);
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if (file.exists() && file.length() > 0) {
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return file.getAbsolutePath();
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}
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try (InputStream is = context.getAssets().open(assetName)) {
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try (OutputStream os = new FileOutputStream(file)) {
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byte[] buffer = new byte[4 * 1024];
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int read;
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while ((read = is.read(buffer)) != -1) {
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os.write(buffer, 0, read);
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}
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os.flush();
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}
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return file.getAbsolutePath();
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} catch (IOException e) {
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Log.e(TAG, "Error process asset " + assetName + " to file path");
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}
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return null;
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}
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@Override
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protected void onCreate(Bundle savedInstanceState) {
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super.onCreate(savedInstanceState);
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final String modelFileAbsoluteFilePath =
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new File(assetFilePath(this, "compute.pt")).getAbsolutePath();
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NativeClient.loadAndForwardModel(modelFileAbsoluteFilePath);
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}
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}

‎NativeApp/app/src/main/java/org/pytorch/nativeapp/NativeClient.java

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package org.pytorch.nativeapp;
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public final class NativeClient {
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public static void loadAndForwardModel(final String modelPath) {
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NativePeer.loadAndForwardModel(modelPath);
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}
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private static class NativePeer {
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static {
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System.loadLibrary("pytorch_nativeapp");
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}
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private static native void loadAndForwardModel(final String modelPath);
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}
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}

‎NativeApp/app/src/main/jniLibs/.gitignore

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*
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*/
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!.gitignore

‎NativeApp/build.gradle

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buildscript {
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repositories {
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google()
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jcenter()
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}
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dependencies {
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classpath 'com.android.tools.build:gradle:3.5.0'
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}
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}
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allprojects {
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repositories {
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google()
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jcenter()
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}
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}
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task clean(type: Delete) {
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delete rootProject.buildDir
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}

‎NativeApp/gradle.properties

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android.useAndroidX=true
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android.enableJetifier=true
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‎NativeApp/make_warp_perspective_pt.py

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import torch
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import torch.utils.cpp_extension
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print(torch.version.__version__)
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op_source = """
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#include <opencv2/opencv.hpp>
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#include <torch/script.h>
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torch::Tensor warp_perspective(torch::Tensor image, torch::Tensor warp) {
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cv::Mat image_mat(/*rows=*/image.size(0),
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/*cols=*/image.size(1),
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/*type=*/CV_32FC1,
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/*data=*/image.data_ptr<float>());
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cv::Mat warp_mat(/*rows=*/warp.size(0),
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/*cols=*/warp.size(1),
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/*type=*/CV_32FC1,
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/*data=*/warp.data_ptr<float>());
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cv::Mat output_mat;
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cv::warpPerspective(image_mat, output_mat, warp_mat, /*dsize=*/{64, 64});
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torch::Tensor output =
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torch::from_blob(output_mat.ptr<float>(), /*sizes=*/{64, 64});
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return output.clone();
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}
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static auto registry =
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torch::RegisterOperators("my_ops::warp_perspective", &warp_perspective);
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"""
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torch.utils.cpp_extension.load_inline(
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name="warp_perspective",
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cpp_sources=op_source,
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extra_ldflags=["-lopencv_core", "-lopencv_imgproc"],
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is_python_module=False,
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verbose=True,
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)
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print(torch.ops.my_ops.warp_perspective)
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@torch.jit.script
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def compute(x, y):
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if bool(x[0][0] == 42):
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z = 5
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else:
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z = 10
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x = torch.ops.my_ops.warp_perspective(x, torch.eye(3))
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return x.matmul(y) + z
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compute.save("app/src/main/assets/compute.pt")

‎NativeApp/settings.gradle

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include ':app'

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